Handbook of logic in artificial intelligence and logic programming (vol. 3)
ACM Computing Surveys (CSUR)
A logic-based theory of deductive arguments
Artificial Intelligence
Quantifying information and contradiction in propositional logic through test actions
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Evaluating significance of inconsistencies
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Coherence measures and their relation to fuzzy similarity and inconsistency in knowledge bases
Artificial Intelligence Review
Computable Models of the Law
On the Definition of Essential and Contingent Properties of Subjective Belief Bases
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Default Logic Based Framework for Argumentation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Measuring conflict and agreement between two prioritized belief bases
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Editorial: Acquiring knowledge from inconsistent data sources through weighting
Data & Knowledge Engineering
Change in abstract argumentation frameworks: adding an argument
Journal of Artificial Intelligence Research
Determining preferences through argumentation
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Presentation of arguments and counterarguments for tentative scientific knowledge
ArgMAS'05 Proceedings of the Second international conference on Argumentation in Multi-Agent Systems
Gradual valuation for bipolar argumentation frameworks
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A problem with these proposals is that they do not consider the believability of the arguments from the perspective of the intended audience. In this paper, we start by reviewing a logic-based framework for argumentation based on argument trees which provide a way of exhaustively collating arguments and counter-arguments. We then extend this framework to it model-theoretic evaluation of the believability of arguments. This extension assumes that the beliefs of a typical member of the audience for argumentation can be represented by a set of classical formulae (a beliefbase). We compare a beliefbase with each argument to evaluate the empathy (or similarly the antipathy) that an agent has for the argument. We show how we can use empathy and antipathy to define a pre-ordering relation over argument trees that captures how nne argument tree is "more believable" than another. We also use these to define criteria for deciding whether an argument at the root of an argument tree is defeated or undeleated given the other arguments in the tree.